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R Programming Fundamentals
Course Description
Overview
Data analysis is crucial to accurately predict the performance of an application. The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you will read ahead and learn all about data visualization and graphics. You will learn data management techniques such as pivots, aggregations, and dealing with missing values. With this various case studies and examples, this course gives you the knowledge to confidently start your career in the field of data science.R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. To start with, you'll understand you how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops.
Once you have grasped the basics, you'll move on to studying data visualization and graphics. You'll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you'll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values.
By the end of this course, you'll have completed an entire data science project of your own for your portfolio or blog.
This is a self-paced video course. Any files needed for exercises or activities will be available for download from the course page. This is a hands-on guide that walks you through concepts with examples using built-in R data. Every topic is enriched with a supporting example that highlights the concept, followed by demos that will gradually build into a full data science project to showcase skills learned. You will perform an end-to-end analysis, starting a data science portfolio.
Objectives
- Use basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control
- Import data to R from various formats, such as CSV, Excel, and SQL
- Clean data by handling missing values and standardizing fields
- Perform univariate and bivariate analysis using ggplot2
- Create statistical summary and advanced plots, such as histograms, scatter plots, box plots, and interaction plots
- Apply data management techniques, such as factors, pivots, aggregation, merging, and dealing with missing values, on the example data sets
Audience
Topics
- Installing R and RStudio
- Installing and Using R Packages
- How to Get Help with R
- R As a Calculator
- Data Structures
- Flow Control
- Data Import and Export
- Base Plots
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LO-094025- Duration: 5
- Delivery Format: Classroom Training, Online Training
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Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Self-Paced Training Terms & Conditions
Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.
After you register for the course, an access instructions email will be sent within 1-2 business days. Within the enrollment period, this self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours per day.
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